DOCSLIB.ORG

Hitachi Solution for Databases in Enterprise Data Warehouse Offload Package for Oracle Database with Mapr Distribution of Apache

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Hitachi Solution for Databases in Enterprise Data Warehouse Offload Package for Oracle Database with Mapr Distribution of Apache Hitachi Solution for Databases in an Enterprise Data Warehouse Offload Package for Oracle Database with MapR Distribution of Apache Hadoop Reference Architecture Guide By Shashikant Gaikwad, Subhash Shinde December 2018 Feedback Hitachi Data Systems welcomes your feedback. Please share your thoughts by sending an email message to [email protected]. To assist the routing of this message, use the paper number in the subject and the title of this white paper in the text. Revision History Revision Changes Date MK-SL-131-00 Initial release December 27, 2018 Table of Contents Solution Overview 2 Business Benefits 2 High Level Infrastructure 3 Key Solution Components 4 Pentaho 6 Hitachi Advanced Server DS120 7 Hitachi Virtual Storage Platform Gx00 Models 7 Hitachi Virtual Storage Platform Fx00 Models 7 Brocade Switches 7 Cisco Nexus Data Center Switches 7 MapR Converged Data Platform 8 Red Hat Enterprise Linux 10 Solution Design 10 Server Architecture 11 Storage Architecture 13 Network Architecture 14 Data Analytics and Performance Monitoring Using Hitachi Storage Advisor 17 Oracle Enterprise Data Workflow Offload 17 Engineering Validation 29 Test Methodology 29 Test Results 30 1 Hitachi Solution for Databases in an Enterprise Data Warehouse Offload Package for Oracle Database with MapR Distribution of Apache Hadoop Reference Architecture Guide Use this reference architecture guide to implement Hitachi Solution for Databases in an enterprise data warehouse offload package for Oracle Database. This Oracle converged infrastructure provides a high performance, integrated, solution for advanced analytics using the following big data applications: . Hitachi Advanced Server DS120 with Intel Xeon Silver 4110 processors . Pentaho Data Integration . MapR distribution for Apache Hadoop This converged infrastructure establishes best practices for environments where you can copy data in an enterprise data warehouse to an Apache Hive database on top of Hadoop Distributed File System (HDFS). Then, you obtain your data for analysis from the Hive database instead of from the busy Oracle server. This reference architecture guide is for you if you are in one of the following roles and need to create a big data management and advanced business analytics platform: . Data scientist . Database administrators . System administrator . Storage administrator . Database performance analyzer . IT professional with the responsibility of planning and deploying an EDW offload solution To use this reference architecture guide to create your big data infrastructure, you should have familiarity with the following: . Hitachi Advanced Server DS120 . Pentaho . Big data and the MapR distribution for Apache Hadoop . Apache Hive . Oracle Real Application Cluster Database 12c Release 1 . IP networks . Red Hat Enterprise Linux 1 2 Note — Testing of this configuration was in a lab environment. Many things affect production environments beyond prediction or duplication in a lab environment. Follow the recommended practice of conducting proof-of-concept testing for acceptable results in a non-production, isolated test environment that otherwise matches your production environment before your production implementation of this solution. Solution Overview Use this reference architecture to implement Hitachi Solution for Databases in an enterprise data warehouse offload package for Oracle Database with the MapR distribution of Apache Hadoop. Business Benefits This solution provides the following benefits: . Improve database manageability You can take a “divide and conquer” approach to data management by moving data onto a lower cost storage tier without disrupting access to data. Extreme scalability Leveraging the extreme scalability of Apache Hadoop Distributed File System, you can offload data from the Oracle servers into commodity servers running big data solutions. Lower total cost of ownership (TCO) Reduce your capital expenditure by reducing the resources needed to run applications. Using Hadoop Distributed File System and low-cost storage makes it possible to keep information that is not deemed currently critical, but that you still might want to access, off the Oracle servers. This approach reduces the number of CPUs needed to run the Oracle database, optimizing your infrastructure. This potentially delivers hardware and software savings, including maintenance and support costs. Reduce the costs of running your workloads by leveraging less expensive, general purpose servers running Hadoop cluster. Improve availability Reduce scheduled downtime by allowing database administrators to perform backup operations on a smaller subset of your data. With offloading, perform daily backups on hot data, and less frequent backups on warm data. For an extremely large database, this can make the difference between having enough time to complete a backup in off-business hours or not. This also reduces the amount of time required to recover your data. Analyze data without affecting the production environment When processing and offloading data through Pentaho Data Integration, dashboards in Pentaho can analyze your data without affecting the performance of the Oracle production environment. 2 3 High Level Infrastructure Figure 1 shows the high-level infrastructure for this solution. The configuration of Hitachi Advanced Server DS120 provides the following characteristics: . Fully redundant hardware . High compute and storage density . Flexible and scalable I/O options . Sophisticated power and thermal design to avoid unnecessary operation expenditures . Quick deployment and maintenance Figure 1 3 4 To avoid any performance impact to the production database, Hitachi Vantara recommends using a configuration with a dedicated IP network for the following: . Production Oracle database . Pentaho server . Hadoop servers Uplink speed to the corporate network depends on your environment and requirements. The Cisco Nexus 93180YC-EX switches can support uplink speeds of 40 GbE. This solution uses Hitachi Unified Compute Platform CI in a solution for an Oracle Database architecture with Hitachi Advanced Server DS220, Hitachi Virtual Storage Platform G600, and two Brocade G620 SAN switches to host Oracle Enterprise Data Warehouse. You can use your existing Oracle database environment or purchase Hitachi Unified Compute Platform CI to host Oracle RAC or a standalone solution to host Enterprise Data Warehouse. Key Solution Components The key solution components for this solution are listed in Table 1, “Hardware Components,” on page 4 and Table 2, “Software Components,” on page 6. TABLE 1. HARDWARE COMPONENTS Hardware Detailed Description Firmware or Driver Quantity Version Hitachi Virtual Storage . One controller 83-04-47-40/00 1 Platform G600 (VSP G600) . 8 × 16 Gb/s Fibre Channel ports . 8 × 12 Gb/s backend SAS ports . 256 GB cache memory . 40 × 960 GB SSDs, plus 2 spares . 16 Gb/s × 2 ports CHB Hitachi Advanced . 2 Intel Xeon Gold 6140 CPU @ 2.30 GHz 3A10.H3 1 Server DS220 (Oracle host) . 768 GB (64GB × 12) DIMM DDR4 synchronous registered (buffered) 2666 MHz . Intel XXV710 Dual Port 25 GbE NIC cards i40e-2.3.6 2 . Emulex LightPulse LPe31002-M6 2-Port 16 Gb/s 11.2.156.27 2 Fibre Channel adapter 4 5 TABLE 1. HARDWARE COMPONENTS (CONTINUED) Hardware Detailed Description Firmware or Driver Quantity Version Hitachi Advanced . 2 Intel Xeon Silver 4110 CPU @ 2.10GHz 3A10.H8 3 Server DS120 (Apache Hadoop . 2 × 128 GB MLC SATADOM for boot host) . 384 GB (32 GB × 12) DIMM DDR4 synchronous registered (buffered) 2666 MHz . Intel XXV710 dual port 25 GbE NIC cards i40e-2.3.6 6 . 1.8 TB SAS drives 4 Hitachi Advanced . 2 Intel Xeon Silver 4110 CPU @ 2.10GHz 3A10.H8 1 Server DS120 (Pentaho host) . 2 × 128 GB MLC SATADOM for boot . 128 GB (32 GB × 4) DIMM DDR4 synchronous registered (buffered) 2666 MHz . Intel XXV710 Dual Port 25 GbE i40e-2.3.6 2 Brocade G620 . 48 port Fibre Channel switch V8.0.1 2 switches . 16 Gb/s SFPs . Brocade hot-pluggable SFP+, LC connector Cisco Nexus . 48 × 10/25 GbE Fiber ports . 7.0(3)I5(1) 2 93180YC-EX switches . 6 × 40/100 Gb/s quad SFP (QSFP28) ports Cisco Nexus 3048TP . 1 GbE 48-Port Ethernet switch . 7.0(3)I4(2) 1 switch 5 6 TABLE 2. SOFTWARE COMPONENTS Software Version Function Red Hat Enterprise Linux Version 7.4 Operating system for the MapR distribution of the Hadoop environment. Kernel Version: kernel-3.10.0- 514.36.5.el7.x86_64 Red Hat Enterprise Linux Version 7.4 Operating system for the Oracle environment 3.10.0-693.11.6.el7.x86_64 Microsoft® Windows Server® 2012 R2 Standard Operating system for the Pentaho Data Integration environment. Oracle 12c Release 1 (12.1.0.2.0) Database software Oracle Grid Infrastructure 12c Release 1 (12.1.0.2.0) Volume management, file system software, and Oracle Automatic Storage Management Pentaho Data Integration 8.1 Extract-transfer-load software MapR distribution for Apache Hadoop 6.0 Hadoop distribution from MapR Apache Hive 1.1.0 Hive database Red Hat Enterprise Linux Device Mapper 1.02.140 Multipath software Multipath Hitachi Storage Navigator [Note 1] Microcode dependent Storage management software Hitachi Storage Advisor [Note 1] 2.1.0 Storage orchestration software [Note 1] These software programs were used for this Oracle Database architecture built on Hitachi Unified Compute Platform CI. They may not be required for your implementation. Pentaho A unified data integration and analytics program, Pentaho addresses the barriers that block your organization's ability to get value from all your data. Simplify preparing and
Load more

Recommended publications
  • Database Solutions on AWS
    Database Solutions on AWS Leveraging ISV AWS Marketplace Solutions November 2016 Database Solutions on AWS Nov 2016 Table of Contents Introduction......................................................................................................................................3 Operational Data Stores and Real Time Data Synchronization...........................................................5 Data Warehousing............................................................................................................................7 Data Lakes and Analytics Environments............................................................................................8 Application and Reporting Data Stores..............................................................................................9 Conclusion......................................................................................................................................10 Page 2 of 10 Database Solutions on AWS Nov 2016 Introduction Amazon Web Services has a number of database solutions for developers. An important choice that developers make is whether or not they are looking for a managed database or if they would prefer to operate their own database. In terms of managed databases, you can run managed relational databases like Amazon RDS which offers a choice of MySQL, Oracle, SQL Server, PostgreSQL, Amazon Aurora, or MariaDB database engines, scale compute and storage, Multi-AZ availability, and Read Replicas. You can also run managed NoSQL databases like Amazon DynamoDB
    [Show full text]
  • Mapr Spark Certification Preparation Guide
    MAPR SPARK CERTIFICATION PREPARATION GUIDE By HadoopExam.com 1 About Spark and Its Demand ........................................................................................................................ 4 Core Spark: ........................................................................................................................................ 6 SparkSQL: .......................................................................................................................................... 6 Spark Streaming: ............................................................................................................................... 6 GraphX: ............................................................................................................................................. 6 Machine Learning: ............................................................................................................................ 6 Who should learn Spark? .............................................................................................................................. 6 About Spark Certifications: ........................................................................................................................... 6 HandsOn Exam: ......................................................................................................................................... 7 Multiple Choice Questions: ......................................................................................................................
    [Show full text]
  • IBM Big SQL (With Hbase), Splice Major Contributor to the Apache Be a Major Determinant“ Machine (Which Incorporates Hbase Madlib Project
    MarketReport Market Report Paper by Bloor Author Philip Howard Publish date December 2017 SQL Engines on Hadoop It is clear that“ Impala, LLAP, Hive, Spark and so on, perform significantly worse than products from vendors with a history in database technology. Author Philip Howard” Executive summary adoop is used for a lot of these are discussed in detail in this different purposes and one paper it is worth briefly explaining H major subset of the overall that SQL support has two aspects: the Hadoop market is to run SQL against version supported (ANSI standard 1992, Hadoop. This might seem contrary 1999, 2003, 2011 and so on) plus the to Hadoop’s NoSQL roots, but the robustness of the engine at supporting truth is that there are lots of existing SQL queries running with multiple investments in SQL applications that concurrent thread and at scale. companies want to preserve; all the Figure 1 illustrates an abbreviated leading business intelligence and version of the results of our research. analytics platforms run using SQL; and This shows various leading vendors, SQL skills, capabilities and developers and our estimates of their product’s are readily available, which is often not positioning relative to performance and The key the case for other languages. SQL support. Use cases are shown by the differentiators“ However, the market for SQL engines on colour of each bubble but for practical between products Hadoop is not mono-cultural. There are reasons this means that no vendor/ multiple use cases for deploying SQL on product is shown for more than two use are the use cases Hadoop and there are more than twenty cases, which is why we describe Figure they support, their different SQL on Hadoop platforms.
    [Show full text]
  • PROCESSING LARGE / BIG DATA SET THROUGH Mapr and PIG
    International Journal of Scientific & Engineering Research Volume 8, Issue 7, July-2017 863 ISSN 2229-5518 PROCESSING LARGE / BIG DATA SET THROUGH MapR AND PIG Arvind Kumar-Senior ERP Solution Architect / Manager, Derex Technologies, Inc. Abstract : We live in the data age. It’s not easy to measure the total volume of data stored electronically, but an IDC estimate put the size of the “digital universe” at 0.18 zettabytes in 2006, and is forecasting a tenfold growth by 2011 to 1.8 zettabytes.* A zettabyte is 1021 bytes, or equivalently one thousand Exabyte’s, one million petabytes, or one billion terabytes. That’s roughly the same order of magnitude as one disk drive for every person in the world. MapReduce is a programming model for data processing. The model is simple, yet nottoo simple to express useful programs in. Hadoop can run MapReduce programs writtenin various languages, MapReduce programs are inherentlyparallel, thus putting very large-scale data analysis into the hands of anyone withenough machines at their disposal. MapReduce comes into its own for large datasets. MapReduce is a framework for performing distributed data processing using the MapReduce programming paradigm. In the MapReduce paradigm, each job has a user-defined map phase, which is a parallel, share-nothing processing of input; followed by a user-defined reduce phase where the output of the map phase is aggregated). Pig raises the level of abstraction for processing large datasets. With MapReduce, thereis a map function and there is a reduce function, and working out how to fit your data processing into this pattern, which often requires multiple MapReduce stages, can be a challenge.
    [Show full text]
  • Your Expert Guide to Hadoop Big Data Platforms
    E-guide Hadoop Big Data Platforms Buyer’s Guide – part 3 Your expert guide to Hadoop big data platforms E-guide In this e-guide A look at Amazon Elastic MapReduce A look at Amazon Elastic cloud-based Hadoop MapReduce cloud-based Hadoop Abie Reifer, DecisionWorx Learn more about the Cloudera The Amazon Elastic MapReduce Web service offers a managed Hadoop distribution Hadoop framework that enables users to distribute and process big data across dynamically scalable Amazon EC2 instances. Inside the Hortonworks open enterprise Hadoop distribution Amazon Elastic MapReduce provides users access to a cloud-based Hadoop implementation for analyzing and processing large amounts of data. Built on top Inside the IBM BigInsights of Amazon's cloud services, EMR leverages Amazon's Elastic Compute Cloud platform for big data and Simple Storage services, enabling users to provision a Hadoop cluster management quickly. Amazon's cloud elasticity and setup tools also give users a way to temporarily Inside the MapR Hadoop distribution for managing big scale up a cloud-based Hadoop cluster for short-term increased computing data capacity. Amazon EMR lets users focus on the design of their workflow without the distractions of configuring a Hadoop cluster. As with other Amazon cloud Inside the Microsoft Azure services, users pay for only what they use. HDInsight cloud infrastructure Page 1 of 28 E-guide In this e-guide Amazon Elastic MapReduce features A look at Amazon Elastic The current version of Amazon EMR, 4.3.0, bundles several open source MapReduce cloud-based applications, a set of components for users to monitor and manage cluster Hadoop resources, and components that enable application and cluster interoperability with other services.
    [Show full text]
  • Amazon Elastic Mapreduce Developer Guide API Version 2009-03-31 Amazon Elastic Mapreduce Developer Guide
    Amazon Elastic MapReduce Developer Guide API Version 2009-03-31 Amazon Elastic MapReduce Developer Guide Amazon Elastic MapReduce: Developer Guide Copyright © 2014 Amazon Web Services, Inc. and/or its affiliates. All rights reserved. The following are trademarks of Amazon Web Services, Inc.: Amazon, Amazon Web Services Design, AWS, Amazon CloudFront, Cloudfront, CloudTrail, Amazon DevPay, DynamoDB, ElastiCache, Amazon EC2, Amazon Elastic Compute Cloud, Amazon Glacier, Kinesis, Kindle, Kindle Fire, AWS Marketplace Design, Mechanical Turk, Amazon Redshift, Amazon Route 53, Amazon S3, Amazon VPC. In addition, Amazon.com graphics, logos, page headers, button icons, scripts, and service names are trademarks, or trade dress of Amazon in the U.S. and/or other countries. Amazon©s trademarks and trade dress may not be used in connection with any product or service that is not Amazon©s, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Elastic MapReduce Developer Guide Table of Contents What is Amazon EMR? .................................................................................................................. 1 What Can You Do with Amazon EMR? ...................................................................................... 2 Hadoop Programming on Amazon EMR ...........................................................................
    [Show full text]
  • Digging Into Hadoop-Based Big Data Architectures
    IJCSI International Journal of Computer Science Issues, Volume 14, Issue 6, November 2017 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784 www.IJCSI.org https://doi.org/10.20943/01201706.5259 52 Digging into Hadoop-based Big Data Architectures Allae Erraissi1, Abdessamad Belangour2 and Abderrahim Tragha3 1,2,3 Laboratory of Information Technology and Modeling LTIM, Hassan II University, Faculty of Sciences Ben M'sik, Casablanca, Morocco will choose between the different solutions relying on Abstract several requirements. For example, he will consider if the During the last decade, the notion of big data invades the field of solution is open source, or if it is a mature one, and so on. information technology. This reflects the common reality that These solutions are Apache projects and therefore organizations have to deal with huge masses of information that available. However, the success of any distribution lies in need to be treated and processed, which represents a strong the suppleness of the installation, the compatibility commercial and marketing challenge. The analysis and collection of Big Data have brought about solutions that combine between the constituents, the support and so on. This work traditional data warehouse technologies with the systems of Big is an advanced analysis of the first comparative study we Data in a logical and coherent structure. Thus, many vendors made before [27]. In this paper, we shall present our offer their own Hadoop distributions such as HortonWorks, second comparative study on the five main Hadoop Cloudera, MapR, IBM Infosphere BigInsights, Pivotal HD, distribution providers in order to explore the benefits and Microsoft HD Insight, and so on.
    [Show full text]
  • From Apache Hadoop/Spark to Amazon EMR: Best Migration Practices and Cost Optimization Strategies Executive Summary
    From Apache Hadoop/Spark to Amazon EMR: Best Migration Practices and Cost Optimization Strategies Executive Summary As more businesses look to technologies like machine learning and predictive analytics to more efficiently drive business outcomes, they seek to migrate on-premises Apache Spark and Hadoop clusters to the cloud. Wrestling with rising costs, maintenance uncertainties and administrative headaches, they turn to Amazon Elastic MapReduce (EMR) to support Big Data, Spark, and Hadoop environments. Amazon EMR is a cost-efficient, high-performance service that improves business agility and keeps down resource utilization costs. As a foundation for building data streaming and machine learning infrastructure, EMR enables organizations to transform into AI-driven enterprises, to become better at targeting prospects, supporting customers, building effective products, and responding to market needs. This whitepaper will walk you through Amazon EMR’s key features, look into its business and technology benefits, and explore core scenarios for migrating Apache Spark & Hadoop clusters to Amazon EMR: Hadoop on-prem to EMR on AWS through Lift & Shift or Re-Architect Next-Gen architecture on AWS — Containers, non-HDFS, or Streaming Existing Hadoop on-prem to Hadoop on AWS through Rehost, Replatform or Redistribution We will also look into major cost optimization strategies with Amazon EMR for businesses running workloads on Apache Hadoop and Apache Spark. 2 Amazon EMR Overview Amazon EMR is the industry-leading cloud-native Big Data platform for improving resource utilization by Apache Hadoop, Hive, Spark, Map/Reduce, and machine learning workloads. Designed to process vast amounts of data quickly and cost-effectively at scale, it is powered by open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi and Presto, coupled with Amazon EC2 and Amazon S3.
    [Show full text]
  • Big Data Fundamentals
    BigBig DataData FundamentalsFundamentals . Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 [email protected] These slides and audio/video recordings of this class lecture are at: http://www.cse.wustl.edu/~jain/cse570-15/ Washington University in St. Louis http://www.cse.wustl.edu/~jain/cse570-15/ ©2015 Raj Jain 18-1 OverviewOverview 1. Why Big Data? 2. Terminology 3. Key Technologies: Google File System, MapReduce, Hadoop 4. Hadoop and other database tools 5. Types of Databases Ref: J. Hurwitz, et al., “Big Data for Dummies,” Wiley, 2013, ISBN:978-1-118-50422-2 Washington University in St. Louis http://www.cse.wustl.edu/~jain/cse570-15/ ©2015 Raj Jain 18-2 BigBig DataData Data is measured by 3V's: Volume: TB Velocity: TB/sec. Speed of creation or change Variety: Type (Text, audio, video, images, geospatial, ...) Increasing processing power, storage capacity, and networking have caused data to grow in all 3 dimensions. Volume, Location, Velocity, Churn, Variety, Veracity (accuracy, correctness, applicability) Examples: social network data, sensor networks, Internet Search, Genomics, astronomy, … Washington University in St. Louis http://www.cse.wustl.edu/~jain/cse570-15/ ©2015 Raj Jain 18-3 WhyWhy BigBig DataData Now?Now? 1. Low cost storage to store data that was discarded earlier 2. Powerful multi-core processors 3. Low latency possible by distributed computing: Compute clusters and grids connected via high-speed networks 4. Virtualization Partition, Aggregate, isolate resources in any size and dynamically change it Minimize latency for any scale 5. Affordable storage and computing with minimal man power via clouds Possible because of advances in Networking Washington University in St.
    [Show full text]
  • Implementing Informatica® Big Data Management 10.2 in an Amazon
    Implementing ® Informatica Big Data Management 10.2 in an Amazon Cloud Environment © Copyright Informatica LLC 2017, 2021. Informatica LLC. Informatica, the Informatica logo, and Informatica Big Data Management are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https:// www.informatica.com/trademarks.html. Abstract You can take advantage of cloud computing efficiencies and power by deploying a Big Data Management solution in the Amazon AWS environment. You can use a hybrid solution to offload or extend on-premises applications to the cloud. You can also use a lift-and-shift strategy to move an existing on-premises big data solution to the Amazon EMR environment to improve processing speed. This article describes the architecture and advantages of migrating your big data solution to Amazon AWS, how to implement the one-click Big Data Management deployment, and how to implement ephemeral clusters, and auto-scaling. Supported Versions • Informatica Big Data Management 10.2 Table of Contents Overview ................................................................. 3 AWS Technology Overview...................................................... 3 AWS Building Blocks........................................................ 3 AWS Regions and Availability Zones.............................................. 4 Networking, Connectivity, and Security............................................. 5 Understanding
    [Show full text]
  • Big Data Solutions Glossary
    Big Data Solutions Reference Glossary (14 pages) Very brief descriptions and links are listed here to provide starting point references for the multitude of Big Data solutions. No endorsements implied. Collected by Bob Marcus Accumulo - (Database, NoSQL, Key-Value) from Apache http://accumulo.apache.org/ Acunu Analytics - (Analytics Tool) on top of Aster Data Platform based on Cassandra http://www.acunu.com/acunu-analytics.html Aerospike - (Database NoSQL Key-Value) http://www.aerospike.com/ Alteryx - (Analytics Tool) http://www.alteryx.com/ Ambari - (Hadoop Cluster Management) from Apache http://incubator.apache.org/ambari/ Analytica - (Analytics Tool) from Lumina http://www.lumina.com/why-analytica/ ArangoDB - (Database, NoSQL, Multi-model) Open source from Europe http://www.arangodb.org/2012/03/07/avocadodbs-design-objectives Aster - (Analytics) Combines SQL and Hadoop on top of Aster MPP Database http://www.asterdata.com/ Asterix - (Database for unstructured data) built on top of Hyracks from UCI http://asterix.ics.uci.edu/ Avro - (Data Serialization) from Apache http://en.wikipedia.org/wiki/Apache_Avro Ayasdi Iris - (Machine Learning) uses Topological Data Analysis http://www.ayasdi.com/product/ Azkaban - (Process Scheduler) for Hadoop http://bigdata.globant.com/?p=441 Azure Table Storage - (Database, NoSQL, Columnar) from Microsoft http://msdn.microsoft.com/en-us/library/windowsazure/jj553018.aspx Behemoth - (Large-scale document processing platform) from Apache http://www.findbestopensource.com/product/behemoth Berkeley DB - (Database)
    [Show full text]
  • Implementation of Hadoop -2
    Implementation of Hadoop -2 Kiranmayi Ganti Performance and Scalability . Data Ingest Using Streaming Writes • MapR uses Spark Streaming to ingest real time data. • Spark Streaming is an extension of the core Spark API that allows enables high- throughput, fault-tolerant stream processing of live data streams. • Data can be ingested from many sources like Kafka, Flume, Twitter, ZeroMQ or plain old TCP sockets and be processed using complex algorithms expressed with high-level functions like map, reduce and join. • Finally, processed data can be pushed out to file systems, databases, and live dashboards. In fact, you can apply Spark’s in-built machine learning algorithms, and graph processing algorithms on data streams. SPARK API Hbase Performance • HBase is an open source, non-relational, distributed database modeled after Google's BigTable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed Filesystem), providing Big Table-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data. • Latency • Consistent low latency Dependability Manageability Ambari • The Apache Ambari project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. • Ambari provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs. Volume Support-I • The MapR File System uses volumes as a unique management entity. • A volume is a logical unit that you create to apply policies to a set of files, directories, tables, and sub-volumes. • You can create volumes for each user, department, or project.
    [Show full text]